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Data X:
200.7 146.5 143.6 141.5 137.5 138.7 135.5 136.4 112.1 109 123.8 151.2 139.2 115.7 147.6 126.1 122.8 137.3 142 137.4 89.4 108 117.7 127.3 121 104.1 119.5 116.7 96.1 125 118.8 114.9 79.3 90.5 87.8 109.4 88.9 97.4 112 86.8 82.9 105.2 89.1 85.5 87.1 85.2 88.2 104 96.4 82.3 114.1 88.9 93.6 101.8 96.6 93.7 68.4 68.7 81.2 85.1 75.4 71.6 83 72.3 90.2 89 84.9 90.9 46.6 55.4 88.7 76 76.9 72.1 90 92.3 78 93.9 84.5 80.4 60.5 75.3 91.5 105.2 92.7
Data Y:
43.5 37.7 36.8 24.4 31.3 43.9 53.6 48.9 30.9 31.8 41.3 43.7 54.1 47.8 36.7 30.8 31.9 61.7 73 64.7 24.2 33.9 32.4 63.2 71.8 60.4 48 44.5 44.9 70.9 72.7 59.5 35.9 40 43.6 57.2 75.8 57.7 47.7 42.3 43 68 70.6 54.2 38.6 40.3 49.2 68.5 75.9 63.2 49.8 37 48.8 74.9 75.3 66.9 44.1 39.8 56.6 77.1 78.5 70.6 54.2 47.2 55.1 74.5 88 80.8 54.4 55.2 73.8 85.3 98.7 86.1 62.5 58.6 67 88.4 96.5 87.1 61.2 62.5 85.2 101.7 113.7
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R Code
n <- length(x) c <- array(NA,dim=c(401)) l <- array(NA,dim=c(401)) mx <- 0 mxli <- -999 for (i in 1:401) { l[i] <- (i-201)/100 if (l[i] != 0) { x1 <- (x^l[i] - 1) / l[i] } else { x1 <- log(x) } c[i] <- cor(x1,y) if (mx < abs(c[i])) { mx <- abs(c[i]) mxli <- l[i] } } c mx mxli if (mxli != 0) { x1 <- (x^mxli - 1) / mxli } else { x1 <- log(x) } r<-lm(y~x) se <- sqrt(var(r$residuals)) r1 <- lm(y~x1) se1 <- sqrt(var(r1$residuals)) bitmap(file='test1.png') plot(l,c,main='Box-Cox Linearity Plot',xlab='Lambda',ylab='correlation') grid() dev.off() bitmap(file='test2.png') plot(x,y,main='Linear Fit of Original Data',xlab='x',ylab='y') abline(r) grid() mtext(paste('Residual Standard Deviation = ',se)) dev.off() bitmap(file='test3.png') plot(x1,y,main='Linear Fit of Transformed Data',xlab='x',ylab='y') abline(r1) grid() mtext(paste('Residual Standard Deviation = ',se1)) dev.off() load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Box-Cox Linearity Plot',2,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'# observations x',header=TRUE) a<-table.element(a,n) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'maximum correlation',header=TRUE) a<-table.element(a,mx) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'optimal lambda(x)',header=TRUE) a<-table.element(a,mxli) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Residual SD (orginial)',header=TRUE) a<-table.element(a,se) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Residual SD (transformed)',header=TRUE) a<-table.element(a,se1) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable.tab')
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